Ancient and modern mechanisms compete in progesterone receptor activation.

The progesterone receptor (PR) belongs to the steroid receptor family of ligand-regulated transcription factors, controlling genes important for development, metabolism, and reproduction. Understanding how diverse ligands bind and modulate PR activity will illuminate the design of ligands that control PR-driven signaling pathways. Here, we use molecular dynamics simulations to investigate how PR dynamics are altered by functionally diverse ligands. Using a library of 33 steroidal ligands that range from inactive to EC50 < 0.1 nM, we reveal an unexpected evolutionary basis for the wide gamut of activation. While other oxosteroid receptors employ an evolutionarily conserved mechanism dependent on a hydrogen bond between the receptor and ligand, extant PR has evolved a preference for activation that is not reliant on this polar interaction. We demonstrate that potent ligands utilize the modern PR mechanism while weaker ligands coopt the defunct ancestral mechanism by forming hydrogen bonds with Asn719. Based on their structures and dynamic signatures, ligands partition into four classes (inactive, weak, moderate and high potency) that interact distinctly with the PR binding pocket. Further, we use luciferase reporter assays and PR mutants to probe the roles of pocket residues in mediating distinct PR mechanisms. This combination of MD simulations and in vitro studies provide insight into how the evolutionary history of PR shapes its response to diverse ligands.

Illuminating ligand-induced dynamics in nuclear receptors through MD simulations.

Nuclear receptors (NRs) regulate gene expression in critical physiological processes, with their functionality finely tuned by ligand-induced conformational changes. While NRs may sometimes undergo significant conformational motions in response to ligand-binding, these effects are more commonly subtle and challenging to study by traditional structural or biophysical methods. Molecular dynamics (MD) simulations are a powerful tool to bridge the gap between static protein-ligand structures and dynamical changes that govern NR function. Here, we summarize a handful of recent studies that apply MD simulations to study NRs. We present diverse methodologies for analyzing simulation data with a detailed examination of the information each method can yield. By delving into the strengths, limitations and unique contributions of these tools, this review provides guidance for extracting meaningful data from MD simulations to advance the goal of understanding the intricate mechanisms by which ligands orchestrate a range of functional outcomes in NRs.